Overview

Dataset statistics

Number of variables51
Number of observations165
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.9 KiB
Average record size in memory408.8 B

Variable types

Categorical28
Numeric23

Alerts

ALP is highly overall correlated with GGTHigh correlation
ALT is highly overall correlated with ASTHigh correlation
AST is highly overall correlated with ALTHigh correlation
Alcohol is highly overall correlated with Grams_dayHigh correlation
Creatinine is highly overall correlated with Encephalopathy and 1 other fieldsHigh correlation
Dir_Bil is highly overall correlated with Encephalopathy and 2 other fieldsHigh correlation
Encephalopathy is highly overall correlated with Creatinine and 1 other fieldsHigh correlation
Ferritin is highly overall correlated with HBeAgHigh correlation
GGT is highly overall correlated with ALPHigh correlation
Grams_day is highly overall correlated with AlcoholHigh correlation
HBeAg is highly overall correlated with Creatinine and 2 other fieldsHigh correlation
INR is highly overall correlated with Total_BilHigh correlation
Iron is highly overall correlated with SatHigh correlation
PHT is highly overall correlated with Spleno and 1 other fieldsHigh correlation
Sat is highly overall correlated with IronHigh correlation
Spleno is highly overall correlated with PHTHigh correlation
Total_Bil is highly overall correlated with Dir_Bil and 2 other fieldsHigh correlation
male_0 is highly overall correlated with male_1High correlation
male_1 is highly overall correlated with male_0High correlation
HBsAg is highly imbalanced (54.1%)Imbalance
HBeAg is highly imbalanced (94.7%)Imbalance
Cirrhosis is highly imbalanced (54.1%)Imbalance
Endemic is highly imbalanced (67.0%)Imbalance
Hemochro is highly imbalanced (74.7%)Imbalance
HIV is highly imbalanced (86.9%)Imbalance
NASH is highly imbalanced (72.0%)Imbalance
Encephalopathy is highly imbalanced (83.5%)Imbalance
Ascites is highly imbalanced (50.3%)Imbalance
Grams_day has 86 (52.1%) zerosZeros
Packs_year has 82 (49.7%) zerosZeros

Reproduction

Analysis started2024-05-23 12:57:10.782558
Analysis finished2024-05-23 12:58:06.407789
Duration55.63 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Symptoms
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
112 
0.0
53 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 112
67.9%
0.0 53
32.1%

Length

2024-05-23T13:58:06.508108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:06.624327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 112
67.9%
0.0 53
32.1%

Most occurring characters

ValueCountFrequency (%)
0 218
44.0%
. 165
33.3%
1 112
22.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 218
66.1%
1 112
33.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 218
44.0%
. 165
33.3%
1 112
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 218
44.0%
. 165
33.3%
1 112
22.6%

Alcohol
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
122 
0.0
43 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 122
73.9%
0.0 43
 
26.1%

Length

2024-05-23T13:58:06.740992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:06.840744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 122
73.9%
0.0 43
 
26.1%

Most occurring characters

ValueCountFrequency (%)
0 208
42.0%
. 165
33.3%
1 122
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 208
63.0%
1 122
37.0%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 208
42.0%
. 165
33.3%
1 122
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 208
42.0%
. 165
33.3%
1 122
24.6%

HBsAg
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
149 
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 149
90.3%
1.0 16
 
9.7%

Length

2024-05-23T13:58:06.957438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:07.057429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 149
90.3%
1.0 16
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 314
63.4%
. 165
33.3%
1 16
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314
95.2%
1 16
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314
63.4%
. 165
33.3%
1 16
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314
63.4%
. 165
33.3%
1 16
 
3.2%

HBeAg
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
164 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 164
99.4%
1.0 1
 
0.6%

Length

2024-05-23T13:58:07.173855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:07.273813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 164
99.4%
1.0 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 329
66.5%
. 165
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329
99.7%
1 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 329
66.5%
. 165
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 329
66.5%
. 165
33.3%
1 1
 
0.2%

HBcAb
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
127 
1.0
38 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 127
77.0%
1.0 38
 
23.0%

Length

2024-05-23T13:58:07.388892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:07.490241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 127
77.0%
1.0 38
 
23.0%

Most occurring characters

ValueCountFrequency (%)
0 292
59.0%
. 165
33.3%
1 38
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 292
88.5%
1 38
 
11.5%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 292
59.0%
. 165
33.3%
1 38
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 292
59.0%
. 165
33.3%
1 38
 
7.7%

HCVAb
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
131 
1.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 131
79.4%
1.0 34
 
20.6%

Length

2024-05-23T13:58:07.606821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:07.712424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 131
79.4%
1.0 34
 
20.6%

Most occurring characters

ValueCountFrequency (%)
0 296
59.8%
. 165
33.3%
1 34
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 296
89.7%
1 34
 
10.3%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 296
59.8%
. 165
33.3%
1 34
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 296
59.8%
. 165
33.3%
1 34
 
6.9%

Cirrhosis
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
149 
0.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 149
90.3%
0.0 16
 
9.7%

Length

2024-05-23T13:58:07.823390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:07.923414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 149
90.3%
0.0 16
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 181
36.6%
. 165
33.3%
1 149
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181
54.8%
1 149
45.2%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181
36.6%
. 165
33.3%
1 149
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181
36.6%
. 165
33.3%
1 149
30.1%

Endemic
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
155 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 155
93.9%
1.0 10
 
6.1%

Length

2024-05-23T13:58:08.039855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:08.139816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 155
93.9%
1.0 10
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 320
64.6%
. 165
33.3%
1 10
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 320
97.0%
1 10
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 320
64.6%
. 165
33.3%
1 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 320
64.6%
. 165
33.3%
1 10
 
2.0%

Smoking
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
104 
0.0
61 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 104
63.0%
0.0 61
37.0%

Length

2024-05-23T13:58:08.256534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:08.356362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 104
63.0%
0.0 61
37.0%

Most occurring characters

ValueCountFrequency (%)
0 226
45.7%
. 165
33.3%
1 104
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226
68.5%
1 104
31.5%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 226
45.7%
. 165
33.3%
1 104
21.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226
45.7%
. 165
33.3%
1 104
21.0%

Diabetes
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
109 
1.0
56 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 109
66.1%
1.0 56
33.9%

Length

2024-05-23T13:58:08.472730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:08.572732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 109
66.1%
1.0 56
33.9%

Most occurring characters

ValueCountFrequency (%)
0 274
55.4%
. 165
33.3%
1 56
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274
83.0%
1 56
 
17.0%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274
55.4%
. 165
33.3%
1 56
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274
55.4%
. 165
33.3%
1 56
 
11.3%

Obesity
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
145 
1.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 145
87.9%
1.0 20
 
12.1%

Length

2024-05-23T13:58:08.689379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:08.789515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 145
87.9%
1.0 20
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310
93.9%
1 20
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

Hemochro
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
158 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 158
95.8%
1.0 7
 
4.2%

Length

2024-05-23T13:58:08.905672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:09.005639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 158
95.8%
1.0 7
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 323
65.3%
. 165
33.3%
1 7
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 323
97.9%
1 7
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 323
65.3%
. 165
33.3%
1 7
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 323
65.3%
. 165
33.3%
1 7
 
1.4%

AHT
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
106 
1.0
59 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 106
64.2%
1.0 59
35.8%

Length

2024-05-23T13:58:09.123963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:09.230222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 106
64.2%
1.0 59
35.8%

Most occurring characters

ValueCountFrequency (%)
0 271
54.7%
. 165
33.3%
1 59
 
11.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 271
82.1%
1 59
 
17.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 271
54.7%
. 165
33.3%
1 59
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 271
54.7%
. 165
33.3%
1 59
 
11.9%

CRI
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
145 
1.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 145
87.9%
1.0 20
 
12.1%

Length

2024-05-23T13:58:09.338986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:09.438917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 145
87.9%
1.0 20
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310
93.9%
1 20
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310
62.6%
. 165
33.3%
1 20
 
4.0%

HIV
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
162 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 162
98.2%
1.0 3
 
1.8%

Length

2024-05-23T13:58:09.555224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:09.655000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 162
98.2%
1.0 3
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 327
66.1%
. 165
33.3%
1 3
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 327
99.1%
1 3
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 327
66.1%
. 165
33.3%
1 3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 327
66.1%
. 165
33.3%
1 3
 
0.6%

NASH
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
157 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157
95.2%
1.0 8
 
4.8%

Length

2024-05-23T13:58:09.771939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:09.871667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157
95.2%
1.0 8
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 322
65.1%
. 165
33.3%
1 8
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 322
97.6%
1 8
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 322
65.1%
. 165
33.3%
1 8
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 322
65.1%
. 165
33.3%
1 8
 
1.6%

Varices
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
121 
0.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 121
73.3%
0.0 44
 
26.7%

Length

2024-05-23T13:58:09.987716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:10.088023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 121
73.3%
0.0 44
 
26.7%

Most occurring characters

ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
63.3%
1 121
36.7%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

Spleno
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
99 
0.0
66 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 99
60.0%
0.0 66
40.0%

Length

2024-05-23T13:58:10.206679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:10.313584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 99
60.0%
0.0 66
40.0%

Most occurring characters

ValueCountFrequency (%)
0 231
46.7%
. 165
33.3%
1 99
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 231
70.0%
1 99
30.0%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 231
46.7%
. 165
33.3%
1 99
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 231
46.7%
. 165
33.3%
1 99
20.0%

PHT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
121 
0.0
44 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 121
73.3%
0.0 44
 
26.7%

Length

2024-05-23T13:58:10.424000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:10.526553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 121
73.3%
0.0 44
 
26.7%

Most occurring characters

ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
63.3%
1 121
36.7%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
42.2%
. 165
33.3%
1 121
24.4%

PVT
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
129 
1.0
36 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 129
78.2%
1.0 36
 
21.8%

Length

2024-05-23T13:58:10.637852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:10.737644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 129
78.2%
1.0 36
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294
89.1%
1 36
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Metastasis
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
129 
1.0
36 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 129
78.2%
1.0 36
 
21.8%

Length

2024-05-23T13:58:10.854212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:10.970713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 129
78.2%
1.0 36
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294
89.1%
1 36
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294
59.4%
. 165
33.3%
1 36
 
7.3%

Hallmark
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
113 
0.0
52 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 113
68.5%
0.0 52
31.5%

Length

2024-05-23T13:58:11.086280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:11.187228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 113
68.5%
0.0 52
31.5%

Most occurring characters

ValueCountFrequency (%)
0 217
43.8%
. 165
33.3%
1 113
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217
65.8%
1 113
34.2%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217
43.8%
. 165
33.3%
1 113
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217
43.8%
. 165
33.3%
1 113
22.8%

Age
Real number (ℝ)

Distinct51
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.690909
Minimum20
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:11.313207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41
Q157
median66
Q374
95-th percentile82.8
Maximum93
Range73
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.319534
Coefficient of variation (CV)0.20589498
Kurtosis0.84618985
Mean64.690909
Median Absolute Deviation (MAD)8
Skewness-0.77998848
Sum10674
Variance177.40998
MonotonicityNot monotonic
2024-05-23T13:58:11.453655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 8
 
4.8%
67 7
 
4.2%
73 7
 
4.2%
64 7
 
4.2%
62 7
 
4.2%
74 6
 
3.6%
76 6
 
3.6%
72 6
 
3.6%
63 6
 
3.6%
52 5
 
3.0%
Other values (41) 100
60.6%
ValueCountFrequency (%)
20 1
0.6%
23 1
0.6%
25 1
0.6%
27 1
0.6%
36 2
1.2%
40 2
1.2%
41 2
1.2%
43 2
1.2%
44 1
0.6%
45 2
1.2%
ValueCountFrequency (%)
93 1
 
0.6%
88 1
 
0.6%
87 2
1.2%
86 1
 
0.6%
85 1
 
0.6%
84 2
1.2%
83 1
 
0.6%
82 3
1.8%
81 2
1.2%
80 3
1.8%

Grams_day
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.351651
Minimum0
Maximum500
Zeros86
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:11.586820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3100
95-th percentile200
Maximum500
Range500
Interquartile range (IQR)100

Descriptive statistics

Standard deviation71.84576
Coefficient of variation (CV)1.4268799
Kurtosis9.1510646
Mean50.351651
Median Absolute Deviation (MAD)0
Skewness2.2844654
Sum8308.0223
Variance5161.8132
MonotonicityNot monotonic
2024-05-23T13:58:11.720239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 86
52.1%
100 32
 
19.4%
200 7
 
4.2%
75 7
 
4.2%
80 5
 
3.0%
50 4
 
2.4%
70 4
 
2.4%
60 2
 
1.2%
180 2
 
1.2%
120 2
 
1.2%
Other values (14) 14
 
8.5%
ValueCountFrequency (%)
0 86
52.1%
4.362395579 × 10-71
 
0.6%
4.143681623 × 10-61
 
0.6%
4.145065469 × 10-61
 
0.6%
0.0008337379778 1
 
0.6%
0.0215001679 1
 
0.6%
20 1
 
0.6%
40 1
 
0.6%
50 4
 
2.4%
60 2
 
1.2%
ValueCountFrequency (%)
500 1
 
0.6%
300 1
 
0.6%
250 1
 
0.6%
200 7
 
4.2%
180 2
 
1.2%
150 1
 
0.6%
137 1
 
0.6%
120 2
 
1.2%
100 32
19.4%
96 1
 
0.6%

Packs_year
Real number (ℝ)

ZEROS 

Distinct62
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.133174
Minimum0
Maximum510
Zeros82
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:11.853286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.8767234 × 10-6
Q316
95-th percentile58.5
Maximum510
Range510
Interquartile range (IQR)16

Descriptive statistics

Standard deviation43.453278
Coefficient of variation (CV)3.074559
Kurtosis104.35637
Mean14.133174
Median Absolute Deviation (MAD)1.8767234 × 10-6
Skewness9.281369
Sum2331.9737
Variance1888.1874
MonotonicityNot monotonic
2024-05-23T13:58:12.012938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
49.7%
30 7
 
4.2%
60 5
 
3.0%
50 5
 
3.0%
40 4
 
2.4%
15 3
 
1.8%
20 2
 
1.2%
48 2
 
1.2%
10 2
 
1.2%
0.002750771032 1
 
0.6%
Other values (52) 52
31.5%
ValueCountFrequency (%)
0 82
49.7%
1.876723401 × 10-61
 
0.6%
1.971746038 × 10-61
 
0.6%
1.528962267 × 10-51
 
0.6%
3.830158966 × 10-51
 
0.6%
6.855707792 × 10-51
 
0.6%
0.0001054181167 1
 
0.6%
0.0001349296561 1
 
0.6%
0.0003110824404 1
 
0.6%
0.0003731806472 1
 
0.6%
ValueCountFrequency (%)
510 1
 
0.6%
80 1
 
0.6%
78 1
 
0.6%
67.5 1
 
0.6%
60 5
3.0%
52.5 1
 
0.6%
50 5
3.0%
48 2
 
1.2%
47 1
 
0.6%
44 1
 
0.6%

PS
Categorical

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
80 
2.0
32 
1.0
30 
3.0
18 
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 80
48.5%
2.0 32
 
19.4%
1.0 30
 
18.2%
3.0 18
 
10.9%
4.0 5
 
3.0%

Length

2024-05-23T13:58:12.136304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:12.252857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 80
48.5%
2.0 32
 
19.4%
1.0 30
 
18.2%
3.0 18
 
10.9%
4.0 5
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 245
49.5%
. 165
33.3%
2 32
 
6.5%
1 30
 
6.1%
3 18
 
3.6%
4 5
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 245
74.2%
2 32
 
9.7%
1 30
 
9.1%
3 18
 
5.5%
4 5
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 245
49.5%
. 165
33.3%
2 32
 
6.5%
1 30
 
6.1%
3 18
 
3.6%
4 5
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 245
49.5%
. 165
33.3%
2 32
 
6.5%
1 30
 
6.1%
3 18
 
3.6%
4 5
 
1.0%

Encephalopathy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2.0
161 
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 161
97.6%
3.0 4
 
2.4%

Length

2024-05-23T13:58:12.386281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:12.504648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 161
97.6%
3.0 4
 
2.4%

Most occurring characters

ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 161
32.5%
3 4
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
50.0%
2 161
48.8%
3 4
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 161
32.5%
3 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 161
32.5%
3 4
 
0.8%

Ascites
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2.0
147 
3.0
18 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 147
89.1%
3.0 18
 
10.9%

Length

2024-05-23T13:58:12.616831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:12.719634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 147
89.1%
3.0 18
 
10.9%

Most occurring characters

ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 147
29.7%
3 18
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
50.0%
2 147
44.5%
3 18
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 147
29.7%
3 18
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 165
33.3%
0 165
33.3%
2 147
29.7%
3 18
 
3.6%

INR
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4184092
Minimum0.84
Maximum4.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:12.846095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.84
5-th percentile1.002
Q11.17
median1.3
Q31.53
95-th percentile2.078
Maximum4.82
Range3.98
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.47274357
Coefficient of variation (CV)0.33329138
Kurtosis19.455338
Mean1.4184092
Median Absolute Deviation (MAD)0.16
Skewness3.6336729
Sum234.03752
Variance0.22348648
MonotonicityNot monotonic
2024-05-23T13:58:13.005166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 7
 
4.2%
1.17 5
 
3.0%
1.24 5
 
3.0%
1.33 5
 
3.0%
1.18 4
 
2.4%
1.32 4
 
2.4%
1.25 4
 
2.4%
1.53 3
 
1.8%
1.13 3
 
1.8%
1.23 3
 
1.8%
Other values (81) 122
73.9%
ValueCountFrequency (%)
0.84 1
 
0.6%
0.94 3
1.8%
0.95 1
 
0.6%
0.96 2
1.2%
0.97 1
 
0.6%
1 1
 
0.6%
1.01 2
1.2%
1.02 1
 
0.6%
1.03 1
 
0.6%
1.04 1
 
0.6%
ValueCountFrequency (%)
4.82 1
0.6%
3.56 1
0.6%
3.16 1
0.6%
3.14 1
0.6%
2.5 1
0.6%
2.42 1
0.6%
2.14 2
1.2%
2.08 1
0.6%
2.07 1
0.6%
2 1
0.6%

AFP
Real number (ℝ)

Distinct140
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18396.547
Minimum1.2
Maximum1810346
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:13.152361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.82
Q15
median29
Q3615
95-th percentile39876.4
Maximum1810346
Range1810344.8
Interquartile range (IQR)610

Descriptive statistics

Standard deviation145471.98
Coefficient of variation (CV)7.9075697
Kurtosis142.9534
Mean18396.547
Median Absolute Deviation (MAD)27
Skewness11.678826
Sum3035430.2
Variance2.1162096 × 1010
MonotonicityNot monotonic
2024-05-23T13:58:13.314173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2 3
 
1.8%
20 3
 
1.8%
3.1 3
 
1.8%
2.5 3
 
1.8%
2.6 3
 
1.8%
1.7 2
 
1.2%
2.8 2
 
1.2%
42 2
 
1.2%
16 2
 
1.2%
18 2
 
1.2%
Other values (130) 140
84.8%
ValueCountFrequency (%)
1.2 1
0.6%
1.5 1
0.6%
1.50007108 1
0.6%
1.50403126 1
0.6%
1.512026491 1
0.6%
1.557801384 1
0.6%
1.7 2
1.2%
1.8 1
0.6%
1.9 1
0.6%
2 2
1.2%
ValueCountFrequency (%)
1810346 1
0.6%
421500 1
0.6%
185203 1
0.6%
100809 1
0.6%
94964 1
0.6%
92421 1
0.6%
50655 1
0.6%
44340 1
0.6%
41470 1
0.6%
33502 1
0.6%

Hemoglobin
Real number (ℝ)

Distinct74
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.878941
Minimum5
Maximum18.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:13.452549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9.5
Q111.5
median13.030937
Q314.6
95-th percentile15.8
Maximum18.7
Range13.7
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.1301398
Coefficient of variation (CV)0.16539711
Kurtosis0.43678038
Mean12.878941
Median Absolute Deviation (MAD)1.569063
Skewness-0.44321084
Sum2125.0253
Variance4.5374954
MonotonicityNot monotonic
2024-05-23T13:58:13.601965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.6 8
 
4.8%
14.9 7
 
4.2%
13.3 5
 
3.0%
14.3 5
 
3.0%
13.1 5
 
3.0%
12.1 5
 
3.0%
13 5
 
3.0%
12.7 4
 
2.4%
15.6 4
 
2.4%
13.5 4
 
2.4%
Other values (64) 113
68.5%
ValueCountFrequency (%)
5 1
 
0.6%
7.3 1
 
0.6%
7.9 1
 
0.6%
8.2 1
 
0.6%
8.9 1
 
0.6%
9.1 2
1.2%
9.2 1
 
0.6%
9.5 3
1.8%
9.7 1
 
0.6%
9.8 2
1.2%
ValueCountFrequency (%)
18.7 1
 
0.6%
16.6 1
 
0.6%
16.4 2
1.2%
16.2 1
 
0.6%
16.1 1
 
0.6%
16 1
 
0.6%
15.9 1
 
0.6%
15.8 2
1.2%
15.7 3
1.8%
15.6 4
2.4%

MCV
Real number (ℝ)

Distinct131
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.052774
Minimum69.5
Maximum119.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:13.751895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum69.5
5-th percentile82.36
Q189.7
median94.8
Q3100.3
95-th percentile107.38
Maximum119.6
Range50.1
Interquartile range (IQR)10.6

Descriptive statistics

Standard deviation8.3560291
Coefficient of variation (CV)0.087909367
Kurtosis0.99298954
Mean95.052774
Median Absolute Deviation (MAD)5.2
Skewness-0.068362697
Sum15683.708
Variance69.823223
MonotonicityNot monotonic
2024-05-23T13:58:13.901839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.1 5
 
3.0%
89.5 4
 
2.4%
96.1 3
 
1.8%
92 3
 
1.8%
93.8 3
 
1.8%
103.6 2
 
1.2%
97.3 2
 
1.2%
90.9 2
 
1.2%
102.3 2
 
1.2%
89.7 2
 
1.2%
Other values (121) 137
83.0%
ValueCountFrequency (%)
69.5 1
0.6%
70.6 1
0.6%
72.2 1
0.6%
74 1
0.6%
78.7 1
0.6%
79.8 1
0.6%
80.2 1
0.6%
81.8 1
0.6%
82.2 1
0.6%
83 1
0.6%
ValueCountFrequency (%)
119.6 1
0.6%
119 1
0.6%
117.3 1
0.6%
111.4 1
0.6%
111.2 1
0.6%
111 1
0.6%
109.3 1
0.6%
109.2 1
0.6%
107.5 1
0.6%
106.9 1
0.6%

Leucocytes
Real number (ℝ)

Distinct108
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499.9285
Minimum2.2
Maximum13000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:14.053791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.54
Q15.1
median7.3
Q324.8
95-th percentile8278
Maximum13000
Range12997.8
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation2907.6121
Coefficient of variation (CV)1.9385004
Kurtosis2.3106942
Mean1499.9285
Median Absolute Deviation (MAD)2.8
Skewness1.829172
Sum247488.2
Variance8454207.9
MonotonicityNot monotonic
2024-05-23T13:58:14.204399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9 6
 
3.6%
5.5 5
 
3.0%
5.4 4
 
2.4%
6.1 4
 
2.4%
4.1 4
 
2.4%
4.3 4
 
2.4%
9.8 3
 
1.8%
6 3
 
1.8%
5.2 3
 
1.8%
7.8 3
 
1.8%
Other values (98) 126
76.4%
ValueCountFrequency (%)
2.2 1
0.6%
2.3 1
0.6%
2.42 1
0.6%
2.5 1
0.6%
2.6 1
0.6%
2.9 1
0.6%
3 1
0.6%
3.2 1
0.6%
3.5 1
0.6%
3.7 1
0.6%
ValueCountFrequency (%)
13000 1
0.6%
10400 1
0.6%
9900 1
0.6%
9600 1
0.6%
9500 1
0.6%
9400 1
0.6%
9100 1
0.6%
8300 2
1.2%
8190 1
0.6%
7200 1
0.6%

Platelets
Real number (ℝ)

Distinct134
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113257.26
Minimum1.71
Maximum459000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:14.351162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.71
5-th percentile77.2
Q1270
median93000
Q3172000
95-th percentile308600
Maximum459000
Range458998.29
Interquartile range (IQR)171730

Descriptive statistics

Standard deviation106775.51
Coefficient of variation (CV)0.94276969
Kurtosis0.49374502
Mean113257.26
Median Absolute Deviation (MAD)91000
Skewness0.94805748
Sum18687448
Variance1.140101 × 1010
MonotonicityNot monotonic
2024-05-23T13:58:14.484454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77000 5
 
3.0%
99 3
 
1.8%
91000 3
 
1.8%
96000 3
 
1.8%
109000 3
 
1.8%
124000 2
 
1.2%
97000 2
 
1.2%
159000 2
 
1.2%
275000 2
 
1.2%
280000 2
 
1.2%
Other values (124) 138
83.6%
ValueCountFrequency (%)
1.71 1
0.6%
51 1
0.6%
58 2
1.2%
60 1
0.6%
61 1
0.6%
68 1
0.6%
70 1
0.6%
77 1
0.6%
78 1
0.6%
80 1
0.6%
ValueCountFrequency (%)
459000 1
0.6%
433000 1
0.6%
412000 1
0.6%
406000 1
0.6%
385000 1
0.6%
351000 1
0.6%
318000 2
1.2%
309000 1
0.6%
307000 1
0.6%
286000 1
0.6%

Albumin
Real number (ℝ)

Distinct47
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4414529
Minimum1.9
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:14.617752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.31
Q13.047616
median3.4
Q34
95-th percentile4.5
Maximum4.9
Range3
Interquartile range (IQR)0.95238404

Descriptive statistics

Standard deviation0.67429597
Coefficient of variation (CV)0.19593352
Kurtosis-0.63874191
Mean3.4414529
Median Absolute Deviation (MAD)0.48
Skewness-0.086582372
Sum567.83972
Variance0.45467506
MonotonicityNot monotonic
2024-05-23T13:58:14.767694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
3.2 12
 
7.3%
3.1 12
 
7.3%
4.2 12
 
7.3%
3.5 9
 
5.5%
4.1 9
 
5.5%
3.8 8
 
4.8%
3.4 8
 
4.8%
2.7 7
 
4.2%
3.6 7
 
4.2%
2.4 6
 
3.6%
Other values (37) 75
45.5%
ValueCountFrequency (%)
1.9 2
 
1.2%
2.1 2
 
1.2%
2.2 3
1.8%
2.3 2
 
1.2%
2.35 1
 
0.6%
2.4 6
3.6%
2.43 1
 
0.6%
2.47 1
 
0.6%
2.6 4
2.4%
2.7 7
4.2%
ValueCountFrequency (%)
4.9 1
 
0.6%
4.8 1
 
0.6%
4.7 2
 
1.2%
4.6 2
 
1.2%
4.54 1
 
0.6%
4.5 4
 
2.4%
4.4 4
 
2.4%
4.3 4
 
2.4%
4.2 12
7.3%
4.1 9
5.5%

Total_Bil
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0252916
Minimum0.3
Maximum40.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:14.917455image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.8
median1.4
Q32.8
95-th percentile9.58
Maximum40.5
Range40.2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.4270914
Coefficient of variation (CV)1.7939069
Kurtosis23.808021
Mean3.0252916
Median Absolute Deviation (MAD)0.7
Skewness4.593477
Sum499.17311
Variance29.453321
MonotonicityNot monotonic
2024-05-23T13:58:15.070563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 11
 
6.7%
1 11
 
6.7%
1.3 10
 
6.1%
0.8 9
 
5.5%
1.4 7
 
4.2%
0.7 7
 
4.2%
0.9 7
 
4.2%
0.6 6
 
3.6%
1.1 5
 
3.0%
1.2 5
 
3.0%
Other values (57) 87
52.7%
ValueCountFrequency (%)
0.3 2
 
1.2%
0.32 1
 
0.6%
0.3660867507 1
 
0.6%
0.4 3
 
1.8%
0.5 11
6.7%
0.6 6
3.6%
0.7 7
4.2%
0.76 1
 
0.6%
0.77 1
 
0.6%
0.8 9
5.5%
ValueCountFrequency (%)
40.5 1
0.6%
32.3 1
0.6%
28.9 1
0.6%
28.5 1
0.6%
19 1
0.6%
16 1
0.6%
10.5 1
0.6%
9.8 1
0.6%
9.6 1
0.6%
9.5 1
0.6%

ALT
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.135959
Minimum11
Maximum420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:15.217124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile18.2
Q131
median50
Q378
95-th percentile163.6
Maximum420
Range409
Interquartile range (IQR)47

Descriptive statistics

Standard deviation57.086316
Coefficient of variation (CV)0.85030909
Kurtosis10.207381
Mean67.135959
Median Absolute Deviation (MAD)22
Skewness2.6175306
Sum11077.433
Variance3258.8475
MonotonicityNot monotonic
2024-05-23T13:58:15.383785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 7
 
4.2%
35 6
 
3.6%
28 5
 
3.0%
43 5
 
3.0%
26 5
 
3.0%
34 4
 
2.4%
25 4
 
2.4%
42 4
 
2.4%
50 4
 
2.4%
54 4
 
2.4%
Other values (87) 117
70.9%
ValueCountFrequency (%)
11 2
1.2%
13 1
0.6%
15 1
0.6%
16 2
1.2%
17 1
0.6%
18 2
1.2%
19 2
1.2%
20 2
1.2%
21 1
0.6%
22 1
0.6%
ValueCountFrequency (%)
420 1
0.6%
299 1
0.6%
262 1
0.6%
217 1
0.6%
207 1
0.6%
204 1
0.6%
195 1
0.6%
178 1
0.6%
164 1
0.6%
162 1
0.6%

AST
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.805086
Minimum17
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:15.534712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile28.2
Q146
median71
Q3108
95-th percentile298
Maximum553
Range536
Interquartile range (IQR)62

Descriptive statistics

Standard deviation86.825133
Coefficient of variation (CV)0.90626852
Kurtosis9.0993122
Mean95.805086
Median Absolute Deviation (MAD)28
Skewness2.7506746
Sum15807.839
Variance7538.6038
MonotonicityNot monotonic
2024-05-23T13:58:15.683262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 6
 
3.6%
38 4
 
2.4%
63 4
 
2.4%
29 4
 
2.4%
52 4
 
2.4%
43 3
 
1.8%
87 3
 
1.8%
47 3
 
1.8%
86 3
 
1.8%
31 3
 
1.8%
Other values (100) 128
77.6%
ValueCountFrequency (%)
17 2
1.2%
19 1
 
0.6%
23 1
 
0.6%
24 1
 
0.6%
26 2
1.2%
27 1
 
0.6%
28 1
 
0.6%
29 4
2.4%
30 1
 
0.6%
31 3
1.8%
ValueCountFrequency (%)
553 1
0.6%
523 1
0.6%
401 1
0.6%
357 1
0.6%
354 1
0.6%
335 1
0.6%
334 1
0.6%
325 1
0.6%
306 1
0.6%
266 1
0.6%

GGT
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.99689
Minimum23
Maximum1575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:15.833451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile40.8
Q192
median180
Q3351
95-th percentile811.8
Maximum1575
Range1552
Interquartile range (IQR)259

Descriptive statistics

Standard deviation257.38419
Coefficient of variation (CV)0.95682963
Kurtosis5.7115693
Mean268.99689
Median Absolute Deviation (MAD)103
Skewness2.0905992
Sum44384.486
Variance66246.622
MonotonicityNot monotonic
2024-05-23T13:58:15.983251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196 3
 
1.8%
80 3
 
1.8%
23 3
 
1.8%
115 3
 
1.8%
82 3
 
1.8%
339 2
 
1.2%
92 2
 
1.2%
67 2
 
1.2%
147 2
 
1.2%
120 2
 
1.2%
Other values (132) 140
84.8%
ValueCountFrequency (%)
23 3
1.8%
33 1
 
0.6%
34 1
 
0.6%
35 1
 
0.6%
38 2
1.2%
40 1
 
0.6%
44 1
 
0.6%
45.3 1
 
0.6%
46 1
 
0.6%
49 1
 
0.6%
ValueCountFrequency (%)
1575 1
0.6%
1390 1
0.6%
993 1
0.6%
983 1
0.6%
924 1
0.6%
879 1
0.6%
869 1
0.6%
833 1
0.6%
816 1
0.6%
795 1
0.6%

ALP
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.00606
Minimum1.28
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:16.135255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile68.4
Q1109
median163
Q3263
95-th percentile582
Maximum980
Range978.72
Interquartile range (IQR)154

Descriptive statistics

Standard deviation172.09865
Coefficient of variation (CV)0.79305917
Kurtosis5.6857754
Mean217.00606
Median Absolute Deviation (MAD)66
Skewness2.2012009
Sum35806
Variance29617.945
MonotonicityNot monotonic
2024-05-23T13:58:16.299941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 5
 
3.0%
97 4
 
2.4%
120 3
 
1.8%
141 3
 
1.8%
113 3
 
1.8%
174 3
 
1.8%
280 2
 
1.2%
117 2
 
1.2%
85 2
 
1.2%
127 2
 
1.2%
Other values (117) 136
82.4%
ValueCountFrequency (%)
1.28 1
0.6%
44 1
0.6%
55 1
0.6%
56 1
0.6%
62 1
0.6%
63 1
0.6%
66 1
0.6%
68 2
1.2%
70 2
1.2%
73 1
0.6%
ValueCountFrequency (%)
980 1
0.6%
974 1
0.6%
923 1
0.6%
708.1525295 1
0.6%
684 1
0.6%
670 1
0.6%
629 1
0.6%
595 1
0.6%
587 1
0.6%
562 1
0.6%

TP
Real number (ℝ)

Distinct57
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.937453
Minimum3.9
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:16.449556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile5.4
Q16.3
median7.1
Q37.573892
95-th percentile8.76
Maximum102
Range98.1
Interquartile range (IQR)1.273892

Descriptive statistics

Standard deviation11.410963
Coefficient of variation (CV)1.2767578
Kurtosis40.688988
Mean8.937453
Median Absolute Deviation (MAD)0.6
Skewness6.2096937
Sum1474.6798
Variance130.21008
MonotonicityNot monotonic
2024-05-23T13:58:16.599220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.3 12
 
7.3%
7 9
 
5.5%
7.1 8
 
4.8%
7.2 8
 
4.8%
6.3 8
 
4.8%
6.7 7
 
4.2%
7.6 7
 
4.2%
6.8 6
 
3.6%
7.5 6
 
3.6%
5.4 5
 
3.0%
Other values (47) 89
53.9%
ValueCountFrequency (%)
3.9 1
 
0.6%
4.3 1
 
0.6%
4.8 1
 
0.6%
4.9 1
 
0.6%
5 3
1.8%
5.2 1
 
0.6%
5.4 5
3.0%
5.5 2
 
1.2%
5.6 4
2.4%
5.7 1
 
0.6%
ValueCountFrequency (%)
102 1
0.6%
78 1
0.6%
69 1
0.6%
58 1
0.6%
37 1
0.6%
25.18242112 1
0.6%
16.8 1
0.6%
9.7 1
0.6%
8.8 1
0.6%
8.6 1
0.6%

Creatinine
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1186028
Minimum0.2
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:16.749406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.534
Q10.7
median0.83
Q31.1
95-th percentile2.688
Maximum7.6
Range7.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.93793025
Coefficient of variation (CV)0.83848371
Kurtosis20.759813
Mean1.1186028
Median Absolute Deviation (MAD)0.15
Skewness4.1373806
Sum184.56947
Variance0.87971316
MonotonicityNot monotonic
2024-05-23T13:58:16.882577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 11
 
6.7%
0.9 10
 
6.1%
0.8 8
 
4.8%
0.77 6
 
3.6%
0.79 4
 
2.4%
1.1 4
 
2.4%
0.83 3
 
1.8%
0.88 3
 
1.8%
0.67 3
 
1.8%
0.71 3
 
1.8%
Other values (81) 110
66.7%
ValueCountFrequency (%)
0.2 1
0.6%
0.38 1
0.6%
0.4 1
0.6%
0.48 2
1.2%
0.52 2
1.2%
0.53 2
1.2%
0.55 2
1.2%
0.56 1
0.6%
0.58 1
0.6%
0.59 2
1.2%
ValueCountFrequency (%)
7.6 1
0.6%
6.1 1
0.6%
4.95 1
0.6%
4.82 1
0.6%
3.95 1
0.6%
3.23 1
0.6%
3.13 1
0.6%
2.82 1
0.6%
2.69 1
0.6%
2.68 1
0.6%

Nodules
Real number (ℝ)

Distinct8
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7183811
Minimum0
Maximum5
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:17.015461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.7944124
Coefficient of variation (CV)0.66010334
Kurtosis-1.6933613
Mean2.7183811
Median Absolute Deviation (MAD)1
Skewness0.33935959
Sum448.53288
Variance3.219916
MonotonicityNot monotonic
2024-05-23T13:58:17.131029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 67
40.6%
5 58
35.2%
2 24
 
14.5%
3 11
 
6.7%
4 2
 
1.2%
1.532693307 1
 
0.6%
0 1
 
0.6%
1.000187645 1
 
0.6%
ValueCountFrequency (%)
0 1
 
0.6%
1 67
40.6%
1.000187645 1
 
0.6%
1.532693307 1
 
0.6%
2 24
 
14.5%
3 11
 
6.7%
4 2
 
1.2%
5 58
35.2%
ValueCountFrequency (%)
5 58
35.2%
4 2
 
1.2%
3 11
 
6.7%
2 24
 
14.5%
1.532693307 1
 
0.6%
1.000187645 1
 
0.6%
1 67
40.6%
0 1
 
0.6%

Major_Dim
Real number (ℝ)

Distinct88
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4068496
Minimum1.5
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:17.250924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.8045727
Q12.4
median4.6
Q38.8
95-th percentile17.4
Maximum22
Range20.5
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.0115358
Coefficient of variation (CV)0.7822153
Kurtosis0.83184459
Mean6.4068496
Median Absolute Deviation (MAD)2.5
Skewness1.294173
Sum1057.1302
Variance25.115491
MonotonicityNot monotonic
2024-05-23T13:58:17.399976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
5.5%
3.5 7
 
4.2%
3 7
 
4.2%
9 6
 
3.6%
2.3 5
 
3.0%
15 5
 
3.0%
10 5
 
3.0%
4 5
 
3.0%
20 4
 
2.4%
2.2 4
 
2.4%
Other values (78) 108
65.5%
ValueCountFrequency (%)
1.5 3
1.8%
1.8 1
 
0.6%
1.800000532 1
 
0.6%
1.800028729 1
 
0.6%
1.800857712 1
 
0.6%
1.801682344 1
 
0.6%
1.803284844 1
 
0.6%
1.809724341 1
 
0.6%
1.847190807 1
 
0.6%
1.851588533 1
 
0.6%
ValueCountFrequency (%)
22 1
 
0.6%
20 4
2.4%
19 1
 
0.6%
18.6 1
 
0.6%
18 1
 
0.6%
17.5 1
 
0.6%
17 1
 
0.6%
16 2
1.2%
15.8 1
 
0.6%
15.7 1
 
0.6%

Dir_Bil
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8190853
Minimum0.1
Maximum29.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:17.531936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.37
median0.7
Q31.4
95-th percentile5.32
Maximum29.3
Range29.2
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation3.9294524
Coefficient of variation (CV)2.1601254
Kurtosis25.292031
Mean1.8190853
Median Absolute Deviation (MAD)0.4
Skewness4.8527942
Sum300.14908
Variance15.440596
MonotonicityNot monotonic
2024-05-23T13:58:17.682069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 19
 
11.5%
0.5 10
 
6.1%
0.7 10
 
6.1%
0.2 8
 
4.8%
1 7
 
4.2%
0.4 6
 
3.6%
0.6 5
 
3.0%
1.1 5
 
3.0%
1.3 4
 
2.4%
0.8 4
 
2.4%
Other values (75) 87
52.7%
ValueCountFrequency (%)
0.1 1
 
0.6%
0.12 1
 
0.6%
0.1421745646 1
 
0.6%
0.2 8
4.8%
0.2165031075 1
 
0.6%
0.2739730902 1
 
0.6%
0.3 19
11.5%
0.3004148362 1
 
0.6%
0.305692963 1
 
0.6%
0.3095974291 1
 
0.6%
ValueCountFrequency (%)
29.3 1
0.6%
22.1 1
0.6%
20.3660334 1
0.6%
19.8 1
0.6%
19.5 1
0.6%
9.7 1
0.6%
9.6 1
0.6%
5.5 2
1.2%
4.6 1
0.6%
4.5 1
0.6%

Iron
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.360108
Minimum0
Maximum224
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:17.831615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.8
Q142.926223
median82
Q3117.08283
95-th percentile184
Maximum224
Range224
Interquartile range (IQR)74.156606

Descriptive statistics

Standard deviation51.04147
Coefficient of variation (CV)0.60504273
Kurtosis-0.38056307
Mean84.360108
Median Absolute Deviation (MAD)38
Skewness0.5915272
Sum13919.418
Variance2605.2316
MonotonicityNot monotonic
2024-05-23T13:58:17.973878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 4
 
2.4%
93 3
 
1.8%
184 3
 
1.8%
87 2
 
1.2%
26 2
 
1.2%
92 2
 
1.2%
180 2
 
1.2%
37 2
 
1.2%
144 2
 
1.2%
15 2
 
1.2%
Other values (137) 141
85.5%
ValueCountFrequency (%)
0 1
0.6%
0.8188187351 1
0.6%
6.74277035 1
0.6%
9 1
0.6%
13 1
0.6%
14 1
0.6%
14.02919073 1
0.6%
15 2
1.2%
19 1
0.6%
19.13123359 1
0.6%
ValueCountFrequency (%)
224 1
 
0.6%
200 1
 
0.6%
197.1623508 1
 
0.6%
197 1
 
0.6%
191.5650861 1
 
0.6%
187.4755101 1
 
0.6%
187 1
 
0.6%
184 3
1.8%
181 1
 
0.6%
180 2
1.2%

Sat
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.854179
Minimum0
Maximum126
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:18.115020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.0025448
Q116.611804
median27.174388
Q349.260444
95-th percentile88.6
Maximum126
Range126
Interquartile range (IQR)32.648641

Descriptive statistics

Standard deviation25.626338
Coefficient of variation (CV)0.73524434
Kurtosis0.70763196
Mean34.854179
Median Absolute Deviation (MAD)15.859563
Skewness1.0582467
Sum5750.9395
Variance656.70918
MonotonicityNot monotonic
2024-05-23T13:58:18.269770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 4
 
2.4%
37 4
 
2.4%
25 4
 
2.4%
56 3
 
1.8%
17 3
 
1.8%
18 3
 
1.8%
21 2
 
1.2%
24 2
 
1.2%
31 2
 
1.2%
33 2
 
1.2%
Other values (127) 136
82.4%
ValueCountFrequency (%)
0 1
0.6%
0.3713628833 1
0.6%
1.52 1
0.6%
2.26 1
0.6%
3 2
1.2%
3.057649226 1
0.6%
4 1
0.6%
5 1
0.6%
5.012724108 1
0.6%
6 2
1.2%
ValueCountFrequency (%)
126 1
0.6%
107.7730523 1
0.6%
99 1
0.6%
96 1
0.6%
95 2
1.2%
94 1
0.6%
90 1
0.6%
89 1
0.6%
87 1
0.6%
86.49187875 1
0.6%

Ferritin
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.71334
Minimum0
Maximum2230
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-05-23T13:58:18.414561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.2
Q1149
median344
Q3642
95-th percentile1000.7592
Maximum2230
Range2230
Interquartile range (IQR)493

Descriptive statistics

Standard deviation378.24327
Coefficient of variation (CV)0.86413466
Kurtosis4.8121713
Mean437.71334
Median Absolute Deviation (MAD)233
Skewness1.7307981
Sum72222.701
Variance143067.97
MonotonicityNot monotonic
2024-05-23T13:58:18.547866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 2
 
1.2%
680.4430021 1
 
0.6%
48.9 1
 
0.6%
490 1
 
0.6%
1316 1
 
0.6%
570.3351277 1
 
0.6%
297 1
 
0.6%
744.8777102 1
 
0.6%
532.9150866 1
 
0.6%
1.576530916 1
 
0.6%
Other values (154) 154
93.3%
ValueCountFrequency (%)
0 1
0.6%
1.576530916 1
0.6%
12.97428364 1
0.6%
14 1
0.6%
16 1
0.6%
18 1
0.6%
20 1
0.6%
22 1
0.6%
28 1
0.6%
29 1
0.6%
ValueCountFrequency (%)
2230 1
0.6%
2165 1
0.6%
1600 1
0.6%
1452 1
0.6%
1316 1
0.6%
1250.871149 1
0.6%
1176 1
0.6%
1127.27522 1
0.6%
1001 1
0.6%
999.7960012 1
0.6%

Class
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
102 
0.0
63 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 102
61.8%
0.0 63
38.2%

Length

2024-05-23T13:58:18.691068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:18.797720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 102
61.8%
0.0 63
38.2%

Most occurring characters

ValueCountFrequency (%)
0 228
46.1%
. 165
33.3%
1 102
20.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 228
69.1%
1 102
30.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 228
46.1%
. 165
33.3%
1 102
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 228
46.1%
. 165
33.3%
1 102
20.6%

male_0
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0.0
133 
1.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 133
80.6%
1.0 32
 
19.4%

Length

2024-05-23T13:58:18.914323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:19.014160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 133
80.6%
1.0 32
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 298
60.2%
. 165
33.3%
1 32
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 298
90.3%
1 32
 
9.7%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 298
60.2%
. 165
33.3%
1 32
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 298
60.2%
. 165
33.3%
1 32
 
6.5%

male_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
1.0
133 
0.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters495
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 133
80.6%
0.0 32
 
19.4%

Length

2024-05-23T13:58:19.130845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-23T13:58:19.230479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 133
80.6%
0.0 32
 
19.4%

Most occurring characters

ValueCountFrequency (%)
0 197
39.8%
. 165
33.3%
1 133
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
66.7%
Other Punctuation 165
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 197
59.7%
1 133
40.3%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 197
39.8%
. 165
33.3%
1 133
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 197
39.8%
. 165
33.3%
1 133
26.9%

Interactions

2024-05-23T13:58:03.249346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:15.299783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:17.453277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:19.712830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:21.757201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:24.109282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:26.292198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:28.622992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:30.687426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:32.825094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.133373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.270730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.469053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:41.658530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:44.009557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.207864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.272579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.354165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:52.916327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:54.967478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:56.965562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:59.080519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:01.112484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:03.343346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:15.413492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:17.548062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:19.812494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:21.861246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:24.210190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:26.374228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:28.707730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:30.790059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:32.918729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.233678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.368757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.565065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:41.752746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:44.109661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.308628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.355776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.439255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:53.014251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:55.067092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:57.065287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:59.180431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:01.212037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:03.429098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:15.497689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:17.625781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:19.896131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:21.954433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:24.292739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:26.474155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:28.788853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:30.870735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:33.002073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.316808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.463343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.649027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:41.827990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:44.192826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.391196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.446934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.537592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:53.085362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:55.150589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:57.148920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:59.270794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:01.295318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:03.510221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:15.582647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:17.719518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:19.979461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:22.048402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:24.392734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:26.557263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:28.891049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:30.970446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:33.085129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.400129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.548195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.746450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:41.911504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:44.292666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.474333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.537928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.620636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:53.185403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:55.233713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:57.248794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:59.346913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:01.397485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:03.609555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:15.681997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:17.811472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:20.076691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:22.146433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:24.492724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:26.657437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:28.988710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:31.070380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:33.185446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.499808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.648056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.848604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:42.012386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:44.392383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.577920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.640859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.721501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:53.285241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-23T13:57:28.536354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:30.613995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:32.735500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:35.050308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:37.181643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:39.377328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:41.561537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:43.926113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:46.125249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:48.189678image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:50.271057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:52.818893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:54.884021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:56.882614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:57:58.997393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:01.028873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-23T13:58:03.160453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-23T13:58:19.380705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AlbuminAFPAHTALPALTASTAgeAlcoholAscitesCRICirrhosisClassCreatinineDiabetesDir_BilEncephalopathyEndemicFerritinGGTGrams_dayHBcAbHBeAgHBsAgHCVAbHIVHallmarkHemochroHemoglobinINRIronLeucocytesMCVMajor_DimMetastasisNASHNodulesObesityPHTPSPVTPacks_yearPlateletsSatSmokingSplenoSymptomsTPTotal_BilVaricesmale_0male_1
Albumin1.000-0.1310.000-0.291-0.072-0.2250.0730.0000.0420.1780.1390.3720.0620.132-0.2930.1810.033-0.006-0.041-0.1510.1340.0000.0000.0000.2370.0000.0000.421-0.4570.027-0.053-0.1580.0950.1270.000-0.1500.0000.3150.2080.2700.0360.149-0.0110.0000.2290.0000.455-0.4060.1880.1080.108
AFP-0.1311.0000.0000.3360.1230.2980.0020.0000.0000.0000.0000.0720.1090.0000.1830.0000.0000.0240.2060.1660.0000.0000.0000.0000.0000.0210.000-0.0640.0460.0040.0550.0470.0650.1660.3200.1990.0000.0530.2750.1660.132-0.0380.0490.0000.0980.0160.0760.2150.1330.1000.100
AHT0.0000.0001.000-0.039-0.146-0.0960.3290.0000.0000.1930.0900.0000.2490.297-0.0870.0000.000-0.0630.003-0.0160.0000.0000.0000.0280.0000.0000.0990.020-0.0760.0950.102-0.0290.1050.0000.0000.0330.0000.0000.0000.0680.0130.0920.0500.0000.1310.092-0.030-0.1550.0740.0530.053
ALP-0.2910.336-0.0391.0000.2620.292-0.0910.1720.0000.0000.4340.341-0.0360.0580.2480.1130.0000.1750.5550.0570.0000.0000.1600.0000.0000.2360.000-0.2700.017-0.0190.235-0.1660.1430.0950.0000.1050.0000.1200.1840.2030.0110.1650.0110.1870.0000.086-0.0760.1260.1590.2290.229
ALT-0.0720.123-0.1460.2621.0000.723-0.2980.0000.0000.0000.0850.0000.0110.1480.2130.1780.2820.1440.407-0.1010.2070.1920.1650.3650.0000.0000.0000.0390.0300.198-0.0310.124-0.0360.0000.3150.0840.1010.0000.0000.091-0.052-0.0660.1850.0000.0000.2110.0780.2290.0000.0000.000
AST-0.2250.298-0.0960.2920.7231.000-0.1860.0000.0000.0000.0000.2800.0620.0000.2440.3270.0000.1080.3940.1100.1670.2670.0000.3790.0000.0530.0000.0040.1370.0580.0300.190-0.0460.0760.0000.1630.0000.0000.1290.1650.014-0.0610.0550.0000.0000.0800.1020.3170.0000.0000.000
Age0.0730.0020.329-0.091-0.298-0.1861.0000.1600.0000.2960.2520.1730.3150.182-0.1380.0880.000-0.094-0.0070.0990.0000.0000.2530.3370.2100.0990.000-0.066-0.109-0.1770.0330.0140.0640.0000.000-0.0480.1660.2680.0000.000-0.0600.103-0.1740.2580.1850.204-0.055-0.1030.0570.3080.308
Alcohol0.0000.0000.0000.1720.0000.0000.1601.0000.0000.0000.4300.000-0.0100.0000.1300.0000.0000.0550.0730.5340.0000.0000.0000.0970.0000.1590.046-0.0940.2450.007-0.0370.210-0.1740.0000.0000.0190.0000.4010.1000.1040.182-0.1530.0060.2640.0000.0000.0320.2400.0990.4190.419
Ascites0.0420.0000.0000.0000.0000.0000.0000.0001.0000.0080.0250.1680.0780.0740.1540.1100.0000.0300.0600.2080.0000.0000.0000.0000.0000.0000.000-0.0550.112-0.040-0.0390.046-0.1080.0000.0000.1400.0080.1230.2250.1490.0440.010-0.0430.0000.1250.112-0.0540.1570.0640.0000.000
CRI0.1780.0000.1930.0000.0000.0000.2960.0000.0081.0000.0590.0000.4220.123-0.0660.0000.000-0.0220.0100.0450.0000.0000.0000.0000.0000.0000.000-0.101-0.164-0.1590.059-0.1010.0860.0000.000-0.0370.0000.0470.1230.0300.0460.078-0.2020.0000.1080.000-0.091-0.2330.0470.0000.000
Cirrhosis0.1390.0000.0900.4340.0850.0000.2520.4300.0250.0591.0000.000-0.0980.0000.1260.0000.000-0.0390.0430.2440.0720.0000.0000.0000.0000.0000.0000.0710.1320.156-0.1230.210-0.3430.0000.0000.1360.0460.2790.0400.0610.151-0.2780.1050.1790.1990.0000.1340.2670.2300.2150.215
Class0.3720.0720.0000.3410.0000.2800.1730.0000.1680.0000.0001.000-0.1250.076-0.1930.1400.000-0.168-0.176-0.1720.0000.0000.0000.0000.0000.0000.0000.311-0.1700.236-0.1050.053-0.0840.2210.000-0.0970.0000.0000.3660.1890.087-0.1200.1310.0000.0000.1920.109-0.1800.0000.0000.000
Creatinine0.0620.1090.249-0.0360.0110.0620.315-0.0100.0780.422-0.098-0.1251.0000.0000.0260.5670.229-0.0200.118-0.0140.0000.9780.2260.1970.0000.0000.000-0.045-0.097-0.0740.1030.036-0.0170.1200.0000.0750.0000.0560.2600.0850.0130.079-0.0810.0920.0600.000-0.084-0.0900.0720.1350.135
Diabetes0.1320.0000.2970.0580.1480.0000.1820.0000.0740.1230.0000.0760.0001.0000.0650.0000.0000.071-0.0080.0750.2440.0000.1510.0000.0000.0840.000-0.2130.0370.047-0.0350.0000.0040.0000.000-0.0380.0000.0000.1830.032-0.0020.0000.1000.0000.0000.0560.0090.0130.0000.0000.000
Dir_Bil-0.2930.183-0.0870.2480.2130.244-0.1380.1300.154-0.0660.126-0.1930.0260.0651.0000.5810.0000.1350.1260.1030.0000.6850.2410.1620.0000.1840.000-0.3200.3100.093-0.0630.207-0.0330.0000.0000.1020.1790.0000.2540.0620.070-0.0560.1220.0000.0530.000-0.1210.5380.0000.0000.000
Encephalopathy0.1810.0000.0000.1130.1780.3270.0880.0000.1100.0000.0000.1400.5670.0000.5811.0000.000-0.031-0.1220.1160.0000.2290.2710.0000.0000.0000.000-0.1300.207-0.0830.0480.116-0.2040.0000.0000.0100.0000.0000.4630.000-0.0090.038-0.0660.0000.0420.000-0.0570.2040.0000.0000.000
Endemic0.0330.0000.0000.0000.2820.0000.0000.0000.0000.0000.0000.0000.2290.0000.0000.0001.000-0.0820.127-0.0410.0000.0000.2940.0460.0000.0450.000-0.033-0.0050.013-0.007-0.0950.1540.0000.0000.1940.0000.0970.0990.0220.019-0.0150.0330.0000.0000.0000.0830.0300.0000.0000.000
Ferritin-0.0060.024-0.0630.1750.1440.108-0.0940.0550.030-0.022-0.039-0.168-0.0200.0710.135-0.031-0.0821.0000.049-0.0260.2320.6720.0640.1540.0000.0000.3370.0870.0370.2120.2030.196-0.0110.1590.230-0.0040.0000.2190.1560.061-0.0660.1530.3510.0000.1030.0000.0060.0510.3380.0000.000
GGT-0.0410.2060.0030.5550.4070.394-0.0070.0730.0600.0100.043-0.1760.118-0.0080.126-0.1220.1270.0491.000-0.0140.0000.0000.0240.1650.0000.0000.000-0.073-0.137-0.0230.2430.0220.0810.1710.2790.1200.0380.0000.0000.1650.0460.154-0.0160.0860.0000.0000.1440.0620.0000.1100.110
Grams_day-0.1510.166-0.0160.057-0.1010.1100.0990.5340.2080.0450.244-0.172-0.0140.0750.1030.116-0.041-0.026-0.0141.0000.0000.4580.0000.0690.0000.0740.000-0.1460.242-0.088-0.0320.146-0.0350.0940.0000.0580.0000.1780.2380.0900.173-0.042-0.0260.0000.0000.000-0.0470.1780.0000.1820.182
HBcAb0.1340.0000.0000.0000.2070.1670.0000.0000.0000.0000.0720.0000.0000.2440.0000.0000.0000.2320.0000.0001.0000.0000.3730.1870.0390.0000.0000.143-0.0520.105-0.0880.076-0.0930.0000.000-0.0010.0000.0000.0000.0000.057-0.1400.1360.0000.0000.0290.094-0.0590.0000.0000.000
HBeAg0.0000.0000.0000.0000.1920.2670.0000.0000.0000.0000.0000.0000.9780.0000.6850.2290.0000.6720.0000.4580.0001.0000.0720.0000.0000.0000.000-0.0480.1260.0570.0390.130-0.1250.0000.000-0.0840.0000.0000.4140.0000.030-0.0350.0980.0000.0000.0000.0990.1280.0000.0000.000
HBsAg0.0000.0000.0000.1600.1650.0000.2530.0000.0000.0000.0000.0000.2260.1510.2410.2710.2940.0640.0240.0000.3730.0721.0000.0000.0000.0000.0000.031-0.024-0.051-0.0010.0760.1180.1280.0000.1370.0460.0000.1550.0000.075-0.098-0.0450.0000.0000.0000.0790.0320.0000.1100.110
HCVAb0.0000.0000.0280.0000.3650.3790.3370.0970.0000.0000.0000.0000.1970.0000.1620.0000.0460.1540.1650.0690.1870.0000.0001.0000.1970.0000.0000.113-0.0680.079-0.0210.014-0.1100.0000.0000.0250.0000.0000.0560.0720.0120.0980.1090.0000.0000.0000.1120.0380.0000.0000.000
HIV0.2370.0000.0000.0000.0000.0000.2100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.1971.0000.0000.0000.195-0.0270.0450.0120.171-0.0330.0000.0000.1250.0000.0000.0000.0000.0160.0850.0360.0000.0000.1280.144-0.1510.0000.0000.000
Hallmark0.0000.0210.0000.2360.0000.0530.0990.1590.0000.0000.0000.0000.0000.0840.1840.0000.0450.0000.0000.0740.0000.0000.0000.0000.0001.0000.000-0.0570.0400.077-0.0970.0720.0500.0000.0000.1830.0000.1130.1880.0000.039-0.1750.1390.0420.0000.003-0.1220.0220.0000.0000.000
Hemochro0.0000.0000.0990.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.3370.0000.0000.0000.0000.0000.0000.0000.0001.0000.0380.0220.1700.0130.038-0.0350.0000.000-0.1180.0000.0000.0000.000-0.004-0.0160.1030.0000.0000.0000.0340.0360.0000.0000.000
Hemoglobin0.421-0.0640.020-0.2700.0390.004-0.066-0.094-0.055-0.1010.0710.311-0.045-0.213-0.320-0.130-0.0330.087-0.073-0.1460.143-0.0480.0310.1130.195-0.0570.0381.000-0.1670.212-0.0180.169-0.1180.0000.000-0.0370.1760.1290.1410.000-0.0150.0380.1320.1280.0000.0000.186-0.0960.1100.0750.075
INR-0.4570.046-0.0760.0170.0300.137-0.1090.2450.112-0.1640.132-0.170-0.0970.0370.3100.207-0.0050.037-0.1370.242-0.0520.126-0.024-0.068-0.0270.0400.022-0.1671.0000.115-0.1050.314-0.2390.0000.000-0.0800.0000.2980.1460.179-0.008-0.1730.0980.0000.2670.000-0.1690.5640.2560.1310.131
Iron0.0270.0040.095-0.0190.1980.058-0.1770.007-0.040-0.1590.1560.236-0.0740.0470.093-0.0830.0130.212-0.023-0.0880.1050.057-0.0510.0790.0450.0770.1700.2120.1151.000-0.0560.279-0.1240.2210.000-0.2010.0000.1170.0000.0000.043-0.2380.8060.0000.2210.1450.0480.2030.0000.0000.000
Leucocytes-0.0530.0550.1020.235-0.0310.0300.033-0.037-0.0390.059-0.123-0.1050.103-0.035-0.0630.048-0.0070.2030.243-0.032-0.0880.039-0.001-0.0210.012-0.0970.013-0.018-0.105-0.0561.000-0.0790.0650.0000.3660.0580.2660.0370.0000.000-0.0360.388-0.0520.0000.1410.0920.036-0.1830.2100.0000.000
MCV-0.1580.047-0.029-0.1660.1240.1900.0140.2100.046-0.1010.2100.0530.0360.0000.2070.116-0.0950.1960.0220.1460.0760.1300.0760.0140.1710.0720.0380.1690.3140.279-0.0791.000-0.2040.2750.000-0.0070.0000.1550.2420.1580.025-0.1960.2680.0790.1900.154-0.0330.3660.0000.2190.219
Major_Dim0.0950.0650.1050.143-0.036-0.0460.064-0.174-0.1080.086-0.343-0.084-0.0170.004-0.033-0.2040.154-0.0110.081-0.035-0.093-0.1250.118-0.110-0.0330.050-0.035-0.118-0.239-0.1240.065-0.2041.0000.3060.136-0.0120.0000.1360.1120.0000.0290.159-0.0620.2210.0500.164-0.038-0.2600.1650.0000.000
Metastasis0.1270.1660.0000.0950.0000.0760.0000.0000.0000.0000.0000.2210.1200.0000.0000.0000.0000.1590.1710.0940.0000.0000.1280.0000.0000.0000.0000.0000.0000.2210.0000.2750.3061.0000.0000.4260.0000.0000.2830.1040.0020.168-0.1390.0000.0000.208-0.152-0.0440.0000.0000.000
NASH0.0000.3200.0000.0000.3150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2300.2790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3660.0000.1360.0001.0000.0440.1070.0390.0000.0000.0080.1870.0510.0000.1070.0000.087-0.0950.2680.0000.000
Nodules-0.1500.1990.0330.1050.0840.163-0.0480.0190.140-0.0370.136-0.0970.075-0.0380.1020.0100.194-0.0040.1200.058-0.001-0.0840.1370.0250.1250.183-0.118-0.037-0.080-0.2010.058-0.007-0.0120.4260.0441.0000.0000.0000.0000.000-0.0250.078-0.1230.0000.0000.156-0.090-0.0010.0000.0180.018
Obesity0.0000.0000.0000.0000.1010.0000.1660.0000.0080.0000.0460.0000.0000.0000.1790.0000.0000.0000.0380.0000.0000.0000.0460.0000.0000.0000.0000.1760.0000.0000.2660.0000.0000.0000.1070.0001.0000.0000.1250.0000.0250.143-0.0040.0000.0000.027-0.0880.0820.0000.0000.000
PHT0.3150.0530.0000.1200.0000.0000.2680.4010.1230.0470.2790.0000.0560.0000.0000.0000.0970.2190.0000.1780.0000.0000.0000.0000.0000.1130.0000.1290.2980.1170.0370.1550.1360.0000.0390.0000.0001.0000.2610.1870.131-0.178-0.0740.1910.5530.000-0.0070.5060.4840.1920.192
PS0.2080.2750.0000.1840.0000.1290.0000.1000.2250.1230.0400.3660.2600.1830.2540.4630.0990.1560.0000.2380.0000.4140.1550.0560.0000.1880.0000.1410.1460.0000.0000.2420.1120.2830.0000.0000.1250.2611.0000.3550.071-0.035-0.0880.0000.0000.208-0.3320.2320.1460.1690.169
PVT0.2700.1660.0680.2030.0910.1650.0000.1040.1490.0300.0610.1890.0850.0320.0620.0000.0220.0610.1650.0900.0000.0000.0000.0720.0000.0000.0000.0000.1790.0000.0000.1580.0000.1040.0000.0000.0000.1870.3551.0000.220-0.071-0.0180.1240.1590.056-0.0280.2470.1510.0000.000
Packs_year0.0360.1320.0130.011-0.0520.014-0.0600.1820.0440.0460.1510.0870.013-0.0020.070-0.0090.019-0.0660.0460.1730.0570.0300.0750.0120.0160.039-0.004-0.015-0.0080.043-0.0360.0250.0290.0020.008-0.0250.0250.1310.0710.2201.000-0.040-0.0230.1610.0000.0560.146-0.0100.0000.0580.058
Platelets0.149-0.0380.0920.165-0.066-0.0610.103-0.1530.0100.078-0.278-0.1200.0790.000-0.0560.038-0.0150.1530.154-0.042-0.140-0.035-0.0980.0980.085-0.175-0.0160.038-0.173-0.2380.388-0.1960.1590.1680.1870.0780.143-0.178-0.035-0.071-0.0401.000-0.1810.0000.3470.0000.133-0.2860.3490.1090.109
Sat-0.0110.0490.0500.0110.1850.055-0.1740.006-0.043-0.2020.1050.131-0.0810.1000.122-0.0660.0330.351-0.016-0.0260.1360.098-0.0450.1090.0360.1390.1030.1320.0980.806-0.0520.268-0.062-0.1390.051-0.123-0.004-0.074-0.088-0.018-0.023-0.1811.0000.0000.2060.157-0.0000.1610.0000.0000.000
Smoking0.0000.0000.0000.1870.0000.0000.2580.2640.0000.0000.1790.0000.0920.0000.0000.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0420.0000.1280.0000.0000.0000.0790.2210.0000.0000.0000.0000.1910.0000.1240.1610.0000.0001.0000.1360.0000.2000.0940.0920.2980.298
Spleno0.2290.0980.1310.0000.0000.0000.1850.0000.1250.1080.1990.0000.0600.0000.0530.0420.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2670.2210.1410.1900.0500.0000.1070.0000.0000.5530.0000.1590.0000.3470.2060.1361.0000.0000.0700.4760.4680.0860.086
Symptoms0.0000.0160.0920.0860.2110.0800.2040.0000.1120.0000.0000.1920.0000.0560.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.1280.0030.0000.0000.0000.1450.0920.1540.1640.2080.0000.1560.0270.0000.2080.0560.0560.0000.1570.0000.0001.000-0.065-0.1010.0730.0000.000
TP0.4550.076-0.030-0.0760.0780.102-0.0550.032-0.054-0.0910.1340.109-0.0840.009-0.121-0.0570.0830.0060.144-0.0470.0940.0990.0790.1120.144-0.1220.0340.186-0.1690.0480.036-0.033-0.038-0.1520.087-0.090-0.088-0.007-0.332-0.0280.1460.133-0.0000.2000.070-0.0651.000-0.1400.1280.0000.000
Total_Bil-0.4060.215-0.1550.1260.2290.317-0.1030.2400.157-0.2330.267-0.180-0.0900.0130.5380.2040.0300.0510.0620.178-0.0590.1280.0320.038-0.1510.0220.036-0.0960.5640.203-0.1830.366-0.260-0.044-0.095-0.0010.0820.5060.2320.247-0.010-0.2860.1610.0940.476-0.101-0.1401.0000.0600.0000.000
Varices0.1880.1330.0740.1590.0000.0000.0570.0990.0640.0470.2300.0000.0720.0000.0000.0000.0000.3380.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.2560.0000.2100.0000.1650.0000.2680.0000.0000.4840.1460.1510.0000.3490.0000.0920.4680.0730.1280.0601.0000.0000.000
male_00.1080.1000.0530.2290.0000.0000.3080.4190.0000.0000.2150.0000.1350.0000.0000.0000.0000.0000.1100.1820.0000.0000.1100.0000.0000.0000.0000.0750.1310.0000.0000.2190.0000.0000.0000.0180.0000.1920.1690.0000.0580.1090.0000.2980.0860.0000.0000.0000.0001.0000.980
male_10.1080.1000.0530.2290.0000.0000.3080.4190.0000.0000.2150.0000.1350.0000.0000.0000.0000.0000.1100.1820.0000.0000.1100.0000.0000.0000.0000.0750.1310.0000.0000.2190.0000.0000.0000.0180.0000.1920.1690.0000.0580.1090.0000.2980.0860.0000.0000.0000.0000.9801.000

Missing values

2024-05-23T13:58:05.891688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-23T13:58:06.191240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SymptomsAlcoholHBsAgHBeAgHBcAbHCVAbCirrhosisEndemicSmokingDiabetesObesityHemochroAHTCRIHIVNASHVaricesSplenoPHTPVTMetastasisHallmarkAgeGrams_dayPacks_yearPSEncephalopathyAscitesINRAFPHemoglobinMCVLeucocytesPlateletsAlbuminTotal_BilALTASTGGTALPTPCreatinineNodulesMajor_DimDir_BilIronSatFerritinClassmale_0male_1
00.01.00.00.00.00.01.00.01.01.00.01.00.00.00.00.01.00.00.00.00.01.067.0137.00000015.0000000.02.02.01.5300095.00000013.700000106.6000004.90000099.000003.4000002.10000034.00000041.000000183.000000150.0000007.100000.7000001.03.50.500000172.33345078.115393680.4430021.00.01.0
11.00.00.00.00.01.01.00.01.01.00.00.01.00.00.00.01.00.00.00.00.01.062.00.00000022.6837190.02.02.01.316061781.13685213.03093795.8086682706.59901739507.998823.2072741.48394577.61266256.304907378.471835236.0653436.165581.3737311.01.80.654251187.47551068.336908530.6465731.01.00.0
20.01.01.00.01.00.01.00.01.00.00.00.01.01.00.00.00.00.01.00.01.01.078.050.00000050.0000002.02.02.00.960005.8000008.90000079.8000008.400000472.000003.3000000.40000058.00000068.000000202.000000109.0000007.000002.1000005.013.00.10000028.0000006.00000016.0000001.00.01.0
31.01.00.00.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.01.077.040.00000030.0000000.02.02.00.950002440.00000013.40000097.1000009.000000279.000003.7000000.40000016.00000064.00000094.000000174.0000008.100001.1100002.015.70.20000071.66425326.132320399.6691560.00.01.0
41.01.01.00.01.00.01.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.076.0100.00000030.0000000.02.02.00.9400049.00000014.30000095.1000006.400000199.000004.1000000.700000147.000000306.000000173.000000109.0000006.900001.8000001.09.00.35818559.00000015.00000022.0000001.00.01.0
50.01.00.00.00.00.01.00.01.00.01.00.00.00.00.00.01.01.01.00.00.01.075.00.0000000.0000001.02.02.01.58000110.00000013.40000091.5000005.40000085.000003.4000003.50000091.000000122.000000242.000000396.0000005.600000.9000001.010.01.40000053.00000022.000000111.0000000.00.01.0
60.00.00.00.01.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.049.00.0000000.0000000.02.02.01.40000138.90000010.400000102.0000003.20000042000.000002.3500002.720000119.000000183.000000143.000000211.0000007.300000.8000005.02.62.190000171.000000126.0000001452.0000000.00.01.0
71.01.00.00.00.00.01.00.01.01.00.00.00.00.00.00.00.01.01.01.00.01.061.00.00083420.0000003.02.02.01.460009860.00000010.80000092.0000003.00000058.000003.1000003.20000079.000000108.000000184.000000300.0000007.100000.5200002.09.01.30000042.00000025.000000706.0000000.00.01.0
81.01.00.00.00.00.01.00.01.01.00.00.01.00.00.00.01.01.01.00.00.01.050.0100.00000032.0000001.02.02.03.140008.80000011.900000107.5000004.90000070.000001.9000003.30000026.00000059.000000115.00000063.0000006.100000.5900001.06.41.20000085.00000073.000000982.0000001.00.01.0
91.01.00.00.00.00.01.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.043.0100.0000000.0000000.02.02.01.120001.80000011.80000087.8000005100.000000193000.000004.2000000.50000071.00000045.000000256.000000303.0000007.100000.5900001.09.30.700000129.13509950.473694336.9123001.00.01.0
SymptomsAlcoholHBsAgHBeAgHBcAbHCVAbCirrhosisEndemicSmokingDiabetesObesityHemochroAHTCRIHIVNASHVaricesSplenoPHTPVTMetastasisHallmarkAgeGrams_dayPacks_yearPSEncephalopathyAscitesINRAFPHemoglobinMCVLeucocytesPlateletsAlbuminTotal_BilALTASTGGTALPTPCreatinineNodulesMajor_DimDir_BilIronSatFerritinClassmale_0male_1
1551.01.00.00.00.00.01.00.01.00.00.01.00.00.00.00.01.01.01.01.00.01.064.075.00000025.0000003.02.03.01.631671.00000012.8101.64.4172000.03.445.838.095.092.0139.07.60.903.09.02.900000111.00000094.0000001600.0000000.00.01.0
1561.01.00.00.01.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.049.00.0000000.0000001.02.02.01.24975.00000015.3103.011.5124000.03.501.262.085.0561.0266.07.50.771.02.30.600000180.00000070.0000001176.0000000.00.01.0
1571.01.00.00.00.00.01.00.01.00.00.00.01.00.00.00.01.01.01.00.00.01.062.00.00000020.0000000.02.02.01.411.70000014.797.26900.072000.03.501.331.024.067.097.07.30.761.03.50.300000117.08282949.776891214.7908361.00.01.0
1581.00.00.00.00.00.01.00.01.01.00.00.00.01.00.00.01.01.01.00.00.01.071.00.0000000.0044973.02.02.01.161713.0000008.294.26.0209000.03.600.362.059.0450.0263.06.81.491.07.50.775370122.98849743.168315352.9698271.00.01.0
1591.01.01.00.01.00.01.00.00.00.00.00.00.00.00.00.01.01.01.00.01.01.064.0100.0000000.0000003.03.03.01.524.9000007.9111.24.167000.02.431.940.071.069.073.04.80.775.03.20.70000040.00000018.000000283.0000000.00.01.0
1600.01.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.040.00.0000000.0000000.02.02.00.841.50007115.4109.29.3184000.04.600.535.040.0449.0109.07.60.705.03.00.332676101.06087548.705253596.0338891.01.00.0
1611.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.01.01.00.00.01.068.00.0000000.0000002.02.02.01.334887.00000012.188.92.5141.03.003.650.091.0147.0280.06.70.701.02.22.30000082.00107825.973099204.8839740.01.00.0
1620.01.00.00.00.00.01.00.01.01.01.00.01.01.00.00.01.01.01.00.00.01.065.00.00000048.0000000.02.02.01.1375.00000013.390.08.0385000.04.300.653.052.0164.0181.07.51.465.018.620.36603323.5864099.252908453.3858651.00.01.0
1630.01.01.00.01.01.01.01.01.00.00.00.00.00.01.00.01.01.01.01.01.01.044.00.0000040.0000022.02.02.02.1494964.00000015.6117.35200.0118000.04.801.150.060.0320.0170.08.40.745.018.01.04865253.46483422.363728391.7097440.00.01.0
1641.01.00.00.00.01.01.00.01.00.00.00.00.00.00.00.01.01.01.00.01.01.052.050.0000000.0000690.02.02.01.7944340.00000012.795.814.4412000.02.2028.578.0127.0444.0462.06.63.955.08.519.80000033.1650079.033764118.1852860.00.01.0